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"""Unit tests for Adaround Activation Sampler"""

import numpy as np

from .models.models_for_tests import simple_relu_model
from aimet_onnx.adaround.activation_sampler import (
    ActivationSampler as AdaroundActivationSampler,
)
from aimet_onnx.quantsim import QuantizationSimModel
from aimet_onnx.utils import CachedDataset
from aimet_onnx.experimental.adascale.activation_sampler import (
    ActivationSampler as AdascaleActivationSampler,
)


class TestAdaroundActivationSampler:
    """
    AdaRound Activation Sampler Unit Test Cases
    """

    def test_activation_sampler_conv(self, tmp_path):
        model = simple_relu_model()
        sim = QuantizationSimModel(model)
        activation_sampler = AdaroundActivationSampler(
            "output", "input", sim, model.model
        )
        data_loader = [np.random.rand(1, 3, 32, 32).astype(np.float32)]
        cached_dataset = CachedDataset(data_loader, 1, tmp_path)
        print(cached_dataset)
        all_inp_data, all_out_data = (
            activation_sampler.sample_and_place_all_acts_on_cpu(cached_dataset)
        )

        # NOTE: Since all inputs are positive, the input and output of relu
        #       should be exactly equal to each other
        assert np.equal(all_out_data, all_inp_data).all()
        assert all_inp_data[0].shape == (1, 3, 32, 32)


class TestAdascaleActivationSampler:
    """
    Adascale Activation Sampler Unit Test Cases
    """

    def test_activation_sampler_relu(self, tmp_path):
        model = simple_relu_model()
        activation_sampler = AdascaleActivationSampler(
            "output", model.model, ["CPUExecutionProvider"]
        )

        data_loader = [np.random.rand(1, 3, 32, 32).astype(np.float32)]
        all_data = activation_sampler.sample_and_place_all_acts_on_cpu(data_loader)
        assert np.equal(all_data, data_loader).all()
        assert all_data[0].shape == (1, 3, 32, 32)
